MD_DataIdentification

The primary purpose of this project was to develop a consistent and accurate surface
elevation dataset derived from high-accuracy Light Detection and Ranging (LiDAR) technology
for the USGS San Francisco Coastal LiDAR project area. The LiDAR data were processed
to a bare-earth digital terrain model (DTM). Detailed breaklines and bare-earth DEMs
were produced for the project area. Data was formatted according to tiles with each
tile covering an area of 1500 m by 1500 m. A total of 712 tiles were produced for
the project encompassing an area of approximately 610 sq. miles. This metadata relates
to LAS that were classified in the following classes: Class 1 = Unclassified. This
class includes vegetation, buildings, noise etc. Class 2 = Ground Class 7= Noise Class
9 = Water Class 10= Ignored Ground (due to proximity to breakline)

These data depict the elevations at the time of the survey and are only accurate for
that time. Users should be aware that temporal changes may have occurred
since this data set was collected and some parts of this data may no longer represent
actual surface conditions. Users should not use this data for critical
applications without a full awareness of its limitations. Any conclusions drawn
from analysis of this information are not the responsibility of NOAA or any of its
partners. These data are NOT to be used for navigational purposes.

Dewberry utilizes a variety of software suites for inventory management, classification,
and data processing. All LiDAR related processes begin by importing the data into
the GeoCue task management software. GeoCue allows the data to retain its delivered
tiling scheme (1500 m by 1500 m). After the a review of the Terrapoint ground classification
was completed, the dataset was processed through a water classification routine that
utilizes breaklines compiled by Dewberry to automatically classify hydrographic features.
The water classification routine selects ground points within the breakline polygons
and automatically classifies them as class 9, water. During this water classification
routine, points which are in close proximity (0.5 m) to the hydrographic features
are moved to class 10, an ignored ground. In addition to classes 1, 2, 9, and 10,
the project allows for a Class 7, noise points. This class was only used if needed
when points could manually be identified as low/high points. The fully classified
dataset is then processed through Dewberry's comprehensive quality control program.
The data was classified as follows: Class 1 = Unclassified. This class includes vegetation,
buildings, noise etc. Class 2 = Ground Class 7= Noise Class 9 = Water Class 10= Ignored
Ground The LAS header information was verified to contain the following: Class (Integer)
GPS Week Time (0.0001 seconds) Easting (0.001 m) Northing (0.001 m) Elevation (0.001
m) Echo Number (Integer 1 to 4) Echo (Integer 1 to 4) Intensity (8 bit integer) Flight
Line (Integer) LiDAR Scan Angle (Integer degree)

1

2012-10-01T00:00:00

The NOAA Coastal Services Center (CSC) received topographic files in LAS format. The
files contained lidar elevation and intensity measurements. The data were received
in UTM Zones 10 coordinates and were vertically referenced to NAVD88 using the Geoid09
model. The vertical units of the data were meters. CSC performed the following processing
for data storage and Digital Coast provisioning purposes: 1. The topographic las files
were converted from orthometric (NAVD88) heights to ellipsoidal heights using Geoid09.
2. The data were converted to LAZ format.

1

2013-01-22T00:00:00

The NOAA National Geophysical Data Center (NGDC) received lidar data files via ftp
transfer from the NOAA Coastal Services Center. The data are currently
being served via NOAA CSC Digital Coast at http://www.csc.noaa.gov/digitalcoast/.
The data can be used to re-populate the system. The data are archived in LAS or LAZ
format.
The LAS format is an industry standard for LiDAR data developed by the American Society
of Photogrammetry and Remote Sensing (ASPRS); LAZ is a loseless compressed version
of
LAS developed by Martin Isenburg (http://www.laszip.org/). The data are exclusively
in geographic coordinates (either NAD83 or ITRF94). The data are referenced vertically
to
the ellipsoid (either GRS80 or ITRF94), allowing for the ability to apply the most
up to date geoid model when transforming to orthometric heights.

1

9999-01-01T00:00:00

- Establishment of survey points to support the LiDAR data collection. Three existing
published CGPS stations (CHAB, P181, P222) were observed in a GPS control network
and used to establish three new points for the primary control for this site. 101U01,101U02,
101U04,AY0887 and AY1499 were observed and used to control all flight missions and
static ground surveys. The following are the final coordinates of the control points
used for this project: Station Id;Latitude;Longitude;Easting;Northing;Ellipsoidal_Height;
1010601; 37 30 52.26391; -122 29 41.86434; -16.6617 1010602; 37 27 16.57733; -122
06 37.48309; -29.7404 1010603; 37 39 48.24857; -122 07 23.10831; -23.1470 1010604;
37 59 35.03464; -122 03 44.26783; -26.2030 1010605; 37 59 49.29391; -122 45 33.01378;
50.3610

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9999-01-01T00:00:00

- Airborne acquisition of Lidar Terrapoint used one Optech ALTM 3100EA system to collect
the data. The Optech System was configured in the following method: Aircraft Speed
150 knots Data Acquisition Height 1300 m AGL Swath Width 755 m Distance between Flight
Lines 377 m Overlap 50 % Scanner Field Of View 19.2 +/- degrees Pulse Repetition Rate
70 KHz LiDAR Scan Frequency 38.7 Hz Number of Returns per Pulse 4 Discrete returns
Beam Divergence 0.3 mRad Flight Line Length <30km Base Station Distance <35km Resultant
Raw Point Density ~2 point pt/m2 with overlap 2 missions (o110292a, o110293a) were
flown at higher altitude with different parameters to accommodate air traffic control
restrictions Aircraft Speed 125 knots Data Acquisition Height 2300 m AGL Overlap 50
% Scanner Field Of View 15 +/- degrees Pulse Repetition Rate 50 KHz LiDAR Scan Frequency
25.5 Hz Number of Returns per Pulse 4 Discrete returns Beam Divergence 0.3 mRad Resultant
Raw Point Density ~2 point pt/m2 with overlap Aircraft platforms were used in the
collection of this project: A Piper Navaho aircraft, registered as FVTL was used to
conduct the aerial survey. The Navaho is a fixed wing aircraft that have an endurance
of approximately 6-7 hours. -GPS-IMU: High accuracy IMU and GPS information concerning
the attitude and position of the sensor were acquired at the same time as the Laser
data. Ground based GPS stations also acquired consecutive GPS information for the
duration of the flights. A combination of Sokkia GSR 2600 and NovAtel DL-4+ dual-frequency
GPS receivers were used to support the airborne operations of this survey. -Number
of Flights A total of 14 missions were flown total under good meteorological and GPS
conditions to provide complete coverage. The LiDAR data were collected under tidal
restrictions at or below Mean Lower Low Water. 10 missions were acquired during spring
2010 (between June 11, 2010 and June 30, 2010) and 4 missions were acquired during
fall (between October 19, 2010 and November 7, 2010)

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9999-01-01T00:00:00

- Airborne GPS Kinematic processing Airborne GPS kinematic data was processed on-site
using GrafNav kinematic On-The-Fly (OTF) software. Flights were flown with a minimum
of 6 satellites in view (13o above the horizon) and with a PDOP of better than 4.
Distances from base station to aircraft were kept to a maximum of 30 km, to ensure
a strong OTF (On-The-Fly) solution. For all flights, the GPS data can be classified
as excellent, with GPS residuals of 3cm average but no larger than 10 cm being recorded.
The Geoid09 geoid model, published by the NGS, was used to transform all ellipsoidal
heights to orthometric.

1

9999-01-01T00:00:00

- Generation and Calibration of laser points Laser data points are generated using
Terrapoint's proprietary laser post-processing software for Midrange data and using
Optech's software Dashmap for data acquired with Optech systems. Those software combine
the raw laser range and angle data file with the finalized GPS/IMU trajectory information.
Each mission is evaluated in Terrasolid's Terramatch software to correct any residual
roll pitch heading misalignments, if necessary those values are to the data. The resulting
point cloud is projected into the desired coordinate system and created in LAS format.
One file per swath. On a project level, a coverage check is carried out to ensure
no slivers are present.

- Data Classification and Editing The data was processed using the software TerraScan,
and following the methodology described herein. The initial step is the setup of the
TerraScan project, which is done by importing project defined tile boundary index
encompassing the entire project areas. The acquired 3D laser point clouds, in LAS
binary format, were imported into the TerraScan project and divided into file size
optimized tiles. Once tiled, the laser points were classified using a proprietary
routine in TerraScan. This routine removes any obvious outliers from the dataset following
which the ground layer is extracted from the point cloud. The ground extraction process
encompassed in this routine takes place by building an iterative surface model. This
surface model is generated using three main parameters: building size, iteration angle
and iteration distance. The initial model is based on low points being selected by
a "roaming window" with the assumption is that these are the ground points. The size
of this roaming window is determined by the building size parameter. The low points
are triangulated and the remaining points are evaluated and subsequently added to
the model if they meet the iteration angle and distance constraints. This process
is repeated until no additional points are added within iteration. A second critical
parameter is the maximum terrain angle constraint, which determines the maximum terrain
angle allowed within the classification model.

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9999-01-01T00:00:00

-Deliverable Product Generation >Tiling Index Classified point cloud products were
delivered in 695 tiles based on provided a tile scheme given by the client. >Raw LIDAR
Point Cloud Raw LiDAR point cloud, was provided in the following formats/parameters:
- LAS V1.2, point record format 1, georeferencing information populated in header
- The following fields are included in the LAS file: 1. Adjusted GPS time reported
to the nearest microsecond 2. Flight line ID 3. Easting (reported to the nearest 0.01m)
4. Northing (reported to the nearest 0.01m) 5. Elevation (reported to the nearest
0.01m) 6. intensity 7. Echo number 8. Classification 9. Scan angle 10. Edge of scan
11. Scan direction - Full swaths, all collected points delivered (except planned cut-off
and discarded flightline) - 1 file per swath, 1 swath per file (except when swath
had to be divided in section for size or calibration) >Classified LIDAR Point Cloud,
tiled Classified LiDAR point cloud, was provided in the following formats/parameters:
- LAS V1.2, point record format 1, georeferencing information populated in header
- The LAS files adhere to the ASPRS classification scheme as outlined below: 1 : Unclassified,
2 : Ground, 7 : Noise, - The following fields are included in the LAS file: 1. Adjusted
GPS time reported to the nearest microsecond 2. Flight line ID 3. Easting (reported
to the nearest 0.01m) 4. Northing (reported to the nearest 0.01m) 5. Elevation (reported
to the nearest 0.01m) 6. intensity 7. Echo number 8. Classification 9. Scan angle
10. Edge of scan 11. Scan direction